Stable Emotional Co-occurrence Patterns Revealed by Network Analysis of Social Media

TL;DR

This study uses network analysis on large-scale social media data to reveal stable patterns in emotion co-occurrence, showing some links strengthen during crises but overall structure remains consistent, advancing understanding of emotional dynamics.

Contribution

It introduces a novel network theory-based computational framework to analyze emotion co-occurrence patterns across crisis and non-crisis periods in social media data.

Findings

Emotion links like Tension strengthen during crises.

The overall emotion network structure remains stable over time.

The framework enables scalable analysis of emotion dynamics.

Abstract

Examining emotion interactions as an emotion network in social media offers key insights into human psychology, yet few studies have explored how fluctuations in such emotion network evolve during crises and normal times. This study proposes a novel computational approach grounded in network theory, leveraging large-scale Japanese social media data spanning varied crisis events (earthquakes and COVID-19 vaccination) and non-crisis periods over the past decade. Our analysis identifies and evaluates links between emotions through the co-occurrence of emotion-related concepts (words), revealing a stable structure of emotion network across situations and over time at the population level. We find that some emotion links (represented as link strength) such as emotion links associated with Tension are significantly strengthened during earthquake and pre-vaccination periods. However, the rank of emotion links remains highly intact. These findings challenge the assumption that emotion co-occurrence is context-based and offer a deeper understanding of emotions' intrinsic structure. Moreover, our network-based framework offers a systematic, scalable method for analyzing emotion co-occurrence dynamics, opening new avenues for psychological research using large-scale textual data.